Results 191 to 200 of about 1,280,497 (284)
Bayesian Integrative Detection of Structural Variations With False Discovery Rate Control. [PDF]
Lian S +9 more
europepmc +1 more source
ABSTRACT The US hemp market is a new and nascent industry that has been devoid of research for about half a century. This study examined the effects of exogenous shock on price at each phase of the value chain—Farm (hemp biomass), and its impact on prices at other phases of the value chain—Intermediary Processor (crude cannabidiol hemp) and Final ...
Solomon Odiase +2 more
wiley +1 more source
Environmental sustainability assessment based on accounting information audit. [PDF]
Hou P, Lu W, Li Q, Wang Q.
europepmc +1 more source
The Geography of Success: A Spatial Analysis of Export Intensity in the Italian Wine Industry
ABSTRACT This paper investigates the paradox of how Italy's fragmented, SME‐dominated wine industry achieves global export success. Moving beyond purely firm‐centric explanations, we test whether export intensity is spatially dependent, clustering geographically in regional ecosystems.
Nicolas Depetris Chauvin, Jonas Di Vita
wiley +1 more source
We retrospectively analyzed clinical data from patients who underwent hepatectomy for hepatocellular carcinoma (HCC) using LCA‐based grading system. These findings provide a new risk stratification framework for the design of precision surgery to treat patients with HCC.
Ling Liu +5 more
wiley +1 more source
A multiscale Bayesian optimization framework for process and material codesign
Abstract The simultaneous design of processes and enabling materials such as solvents, catalysts, and adsorbents is challenging because molecular‐ and process‐level decisions are strongly interdependent. Sequential approaches often yield suboptimal results since improvements in material properties may not translate into superior process performance. We
Michael Baldea
wiley +1 more source
Bayesian-calibrated global sensitivity analysis for mathematical models using generative AI. [PDF]
Wang X.
europepmc +1 more source
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi +2 more
wiley +1 more source
Reliable uncertainty estimates in deep learning with efficient Metropolis-Hastings algorithms. [PDF]
Schmal M, Mäder P.
europepmc +1 more source

